AWS-CodeGuru

• CodeGuru is a fully managed service that helps proactively improve code quality and application performance with intelligent recommendations.

• It includes CodeGuru Reviewer and CodeGuru Profiler.

CodeGuru Profiler

• CodeGuru Profiler analyzes the performance of their applications as they run

• It can identify when an application is consuming excessive CPU capacity on a logging routine instead of executing core business logic.

• It is designed to profile an application continuously in production, with a minimal footprint.

• It analyzes the application runtime profile and provides intelligent recommendations and visualizations that guide developers on how to improve the performance of the most relevant parts of their code.

• It is designed to collect data on everything that happened in that application’s behavior, regardless of scenarios.

• It uses a knowledge base of commonly encountered performance inefficiencies to automatically discover code patterns in users live application that impact its performance.

• It works with applications hosted on Amazon EC2, containerized applications running on Amazon ECS and Amazon EKS, as well as serverless applications running on AWS Fargate.

• It currently supports Java applications.

• It consists of three parts: an agent, the profiler service & intelligent recommendations.

• A profiler group is a logical grouping created by users. It represents the boundary of one application.

CodeGuru Reviewer

• It analyzes code pull requests on users code repositories  .

• It will automatically provide intelligent recommendations as comments on users pull requests generated for the connected repositories.

• It is an automated code review service that identifies critical defects and deviation from AWS best practices for Java-based code.

• It scans the lines of code within a pull request or code repository and provides intelligent recommendations based on standards learned from major open source projects as well as Amazon codebase.

• It currently supports Java code stored in GitHub and AWS CodeCommit repositories.

• It will need read-only access and the ability to post comments on the Pull Requests.

• It does not store users source code.

• It is trained using rule mining and supervised machine learning models that use a combination of logistic regression and neural networks.

• It uses the feedback users provide as labels and iteratively improves the quality of code detectors.